Methods and apparatus for multiple input multiple output (MIMO) successive interference cancellation (SIC)

ABSTRACT

Systems and methods are provided for determining a successive interference cancellation (SIC) decoding ordering in a multiple input multiple output transmission (MIMO) system. Multiple decoding orderings for received codewords are identified. A performance objective, such as total throughput or total probability of decoding, is computed for each one of the orderings based on the position of the codewords in the ordering. A globally optimal ordering is found that maximizes the performance objective over the multiple decoding orderings.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/164,111, filed Jun. 20, 2011, (now U.S. Pat. No. 8,630,379), whichclaims benefit under 35 U.S.C. §119(e) of U.S. Provisional ApplicationNo. 61/357,875, filed Jun. 23, 2010, each of which is herebyincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of theinventors hereof, to the extent the work is described in this backgroundsection, as well as aspects of the description that may not otherwisequalify as prior art at the time of filing, are neither expressly norimpliedly admitted as prior art against the present disclosure.

The disclosed technology relates to communication systems, and moreparticularly, to performing successive interference cancellation (SIC)in multiple input multiple output (MIMO) systems.

In a data transmission system, it is desirable for information, oftengrouped into packets, to be accurately received at a destination. Atransmitter at or near the source sends the information provided by thesource via a signal or signal vector. A receiver at or near thedestination processes the signal sent by the transmitter. The medium, ormedia, between the transmitter and receiver, through which theinformation is sent, may corrupt the signal such that the receiver isunable to correctly reconstruct the transmitted information. Therefore,given a transmission medium, sufficient reliability is obtained throughcareful design of the transmitter and/or receiver, and of theirrespective components.

Successive interference cancellation (SIC) is one technique forimproving the performance of a data transmission system. According tothis technique, a received codeword that is associated with strongchannel conditions is decoded before other codewords that are associatedwith weak channel conditions. Effects of the decoded codeword aresubtracted from a received signal vector to eliminate interference dueto the decoded codeword from the other codewords. In this way, the othercodewords may experience less interference and are able to achieve ahigher Signal-to-Noise Ratio (SNR) than without interferencecancellation.

The order in which codewords are decoded may have an important impact onthe SIC performance. Conventional SIC methods decode codewords in anorder that is based on channel parameters (e.g., channel quality)associated with each codeword. These conventional approaches provide alocally optimal decoding order because such approaches select, at eachdecoding stage, a best codeword for that stage. Furthermore, theseapproaches operate on the assumption of perfect interferencecancellation, which is not always true. However, a codeword that islocally optimal at a given decoding stage may not guarantee the largestinterference cancellation gain for the remaining codewords.

SUMMARY OF THE INVENTION

In view of the foregoing, systems and methods are provided forperforming successive interference cancellation (SIC) in a multipleinput multiple output transmission (MIMO) system.

In some embodiments, a plurality of SIC decoding orderings of receivedcodewords is identified. Each ordering of the plurality of orderingsdefines a unique order of the codewords for decoding using SIC. For eachordering of the plurality of orderings, a performance objective may becomputed based on a position of each codeword in each ordering. Anoptimal ordering is determined that maximizes the performance objectiveover the plurality of orderings.

In some implementations, the performance objective may be evaluated bycomputing, for each ordering of the plurality of orderings, aperformance component for each codeword based on a position of thecodeword in the ordering.

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects and potential advantages of the presentdisclosure will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 is a high level block diagram of a data transmission system inaccordance with embodiments of the present disclosure;

FIG. 2 is a high level block diagram of a successive interferencecancellation (SIC) system in accordance with embodiments of the presentdisclosure;

FIG. 3 is a flow diagram illustrating a process for performing SIC inaccordance with embodiments of the present disclosure;

FIG. 4 illustrates a process for determining a globally optimal SICordering for codewords in accordance with embodiments of the presentdisclosure;

FIG. 5 is a flow diagram illustrating a process to determine a SICordering using performance components predicted from channelinformation, in accordance with embodiments of the present disclosure;

FIG. 6A is a flow diagram illustrating a process for generating a treeto compute performance components of codewords with reduced complexityin a SIC process in accordance with embodiments of the presentdisclosure;

FIG. 6B is an exemplary tree of performance components of codewordsillustrating the process of FIG. 6A in accordance with embodiments ofthe present disclosure;

FIG. 7A is a flow diagram illustrating a process for generating a treeto compute performance components of codewords with further reducedcomplexity in a SIC process in accordance with embodiments of thepresent disclosure; and

FIG. 7B is an exemplary tree of performance components of codewordsillustrating the process of FIG. 7A in accordance with embodiments ofthe present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure generally relates to performing successiveinterference cancellation (SIC) in a multiple input multiple outputtransmission (MIMO) system. In one aspect, a plurality of SIC orderingsof received codewords is determined. A performance objective, such astotal throughput or total probability of decoding, is computed for eachone of the orderings based on the position of the codewords in theordering. For example, performance components are computed for each oneof the codewords in each one of the orderings. A globally optimalordering is found that maximizes the performance objective over theplurality of orderings. The complexity of finding the globally optimalordering may be decreased using various techniques, such as a treerepresentation.

FIG. 1 shows an illustration of a data transmission system 100 inaccordance with some embodiments. The system of FIG. 1 includestransmitter 110, channel 160, and receiver 180. In some embodiments,data to be transmitted may be divided between a large number oftransmission systems such as system 100, where each system correspondsto one parallel transmission. For example, system 100 may correspond toone subcarrier that carries data in a particular frequency range, or atone. In some embodiments, the illustrated system may represent awireless communication system. In these embodiments, transmitter 110 maybe a wireless router and receiver 180 may be a wireless receiver, suchas a mobile telephone, computer, laptop, hand held device, or other suchdevice. The components shown in transmitter 110 and receiver 180 may beimplemented by a single integrated circuit (IC) or as separatecomponents in a circuit board or implemented on a programmable logicdevice. These components may be implemented on separate devices orcircuits and networked together.

Transmitter 110 may process C information bit sequences to produce Ccodewords using encoder and modulator blocks. For example, encoder andmodulator blocks 102, 104, and 106 may process bit sequences 101, 103,and 105, to output codewords 112, 114, and 116, respectively. Althoughthe present disclosure is described in terms of binary data, the bitsequences 101, 103, and 105 may be replaced with a sequence ofnon-binary digits or another type of information-containing symbolwithout departing from the scope of the present disclosure. In someembodiments, encoder and modulator blocks 102, 104, and 106 may includeencoders 102 a, 104 a, and 106 a respectively, e.g., that employ errorcorrection or error detection codes to encode bit sequences 101, 103,and 105. For example, encoders 102 a, 104 a, and 106 a may encode bitsequences 101, 103, and 105 using CRC code, convolutional code, Turbocode, LDPC code, or any other suitable code. Encoder and modulatorblocks 102, 104, and 106 may additionally include modulators 102 b, 104b, and 106 b respectively, to modulate the encoded bit sequences of bitsequences 101, 103 and 105 based on any appropriate modulation scheme,such quadrature amplitude modulation (QAM), pulse amplitude modulation(PAM), or phase shift keying (PSK). Although encoder and modulatorblocks 102, 104, and 106 are illustrated as separate blocks, the blocksmay be implemented as one or multiple encoder and modulator units.

Codeword to stream mapper 120 may process the C codewords output by theencoder and modulator blocks (e.g., encoder and modulator blocks 112,114, and 116) to output S streams. These S streams are represented bycoded values x₁[m] through x_(s)[m], where m is a transmission indexassociated with a transmission instance. A transmission instance may bedefined in time domain or frequency domain or any combination thereof.In some embodiments, m may refer to the index of symbols sent in thetime domain. In some embodiments, m may represent the index ofsubcarriers (i.e., m=1 indexes a stream that is transmitted first, e.g.,by a first subcarrier and m=2 indexes a stream that is transmittedsecond by a second, possibly different, subcarrier). All S streams x₁[m]through x_(s)[m] may be collectively referred to as a S×1 stream vectorx(m) such that:x(m)=[x ₁ [m], . . . ,x _(s) [m]] ^(T).

Streams x₁[m] through x_(s)[m] may be input into MIMO precoder 150. MIMOprecoder 150 may map stream x₁[m] through x_(s)[m] to transmit values{tilde over (x)}₁[m] through {tilde over (x)}_(T)[m], where T is thenumber of transmit antennas (T≧S). These transmit values may be groupedin a T×1 vector {tilde over (x)}(m), which will be referred tohereinafter as transmit vector {tilde over (x)}(m) where:{tilde over (x)}(m)=[{tilde over (x)} ₁ [m], . . . ,{tilde over (x)}_(T) [m]] ^(T).

This mapping from stream vector x to transmit vector {tilde over (x)}may be performed using a linear precoding operation. For example, MIMOprecoder 150 may generate transmit vector {tilde over (x)} bymultiplying stream vector {tilde over (x)} by a T×S precoding matrix P,such that:{tilde over (x)}(m)=P(m)x(m).  (EQ. 1)Precoding matrix P may be chosen to implement certain transmissionschemes. In some embodiments, precoding matrix P may be selected suchthat multiple copies of the same data stream x₁[m] are sent across anumber of transmit antennas to improve the reliability of data transfer.This redundancy results in a higher chance of being able to use one ormore of the received copies to reconstruct the transmitted signals atthe receiver.

Transmit values {tilde over (x)}₁[m] through {tilde over (x)}₁[m] may betransmitted using T transmit antennas through channel 160 and receivedby R receiver antennas at receiver 180. For example, {tilde over(x)}₁[m] may be transmitted through transmit antenna 152. Duringtransmission, {tilde over (x)}₁[m] through {tilde over (x)}_(T)[m] maybe altered by a transmission medium, represented by channel 160, andadditive noise sources z₁[m] through z_(R)[m]. In a wirelesscommunication system channel 160 may be the physical space between thetransmit and receiver antennas, which obstructs and attenuates thetransmitted signals due to at least time varying multipath fades andshadowing effects. Additive noise sources z₁[m] through z_(R)[m] may,for example, be ambient electromagnetic interference. In some scenarios,noise sources z₁[m] through z_(R)[m] may be modeled as additive whiteGaussian noise (AWGN) with zero mean. Also, in many applications,channel 160 may be time invariant, meaning that the properties of thechannel do not substantially change over an appropriate time scale. Inreal time data transmission systems, an appropriate time scale may be inthe millisecond range.

Receiver 180 may receive signals y₁[m] through y_(R)[m] using R receiverantennas such as receiver antenna 182. These received signals will becollectively referred to as the m^(th) received vector y(m), or simplythe received vector y, where:y(m)=[y ₁ [m], . . . ,y _(R) [m]] ^(T).Receiver 180 may include any suitable number of receiver antennas, andtherefore R may be any integer of at least one. Signals y₁[m] throughy_(R)[m] may include information from one or more of signals {tilde over(x)}₁[m] through {tilde over (x)}_(T)[m] that have been attenuatedand/or corrupted by channel 160 and noise sources z₁[m] throughz_(R)[m]. Receiver 180 may process the received signals to produceoutput bit sequence 191. The processing done by receiver 180 may includedemodulation and decoding. Alternatively, output bit sequence 191 may bedirected to a decoder (not shown) external to receiver 180.

Because of the multiple transmit antennas of transmitter 110 and thepossibly multiple receiver antennas of receiver 180, channel 160 maysometimes be referred to as a MIMO channel with T inputs (fromtransmitter 110) and R outputs (to receiver 180), or simply a T×R MIMOchannel. Due to channel properties, the signal received by each of the Rreceiver antennas may be based on signals from multiple transmitantennas. In particular, a signal received by each receiver antenna maybe a linear combination of the signals provided by the transmitantennas. Thus, in matrix form, the m^(th) received vector r(m) can bemodeled by:y(m)={tilde over (H)}(m){tilde over (x)}(m)+z(m),m=1, . . . ,M  (EQ. 2),where M is the total number of received coded symbol vectors, y is theR×1 received vector representing the signals received by the R receiverantennas of receiver 180, and {tilde over (H)} is a T×R matrixrepresenting the effect of channel 160 on transmit vector {tilde over(x)}, and may sometimes be referred to as a channel response matrix.Vector {tilde over (x)} is a T×1 vector containing the transmit valuestransmitted by the T transmit antennas of transmitter 110, and z is anR×1 signal vector representing additive noise, where z(m)=[z₁[m], . . ., z_(R)[m]]^(T).

Substituting EQ. 1 into EQ. 2, one can compute an effective transmissionchannel relating the stream vector x to the received vector y asfollows:y(m)={tilde over (H)}(m)P(m)x(m)+z(m)=H(m)x(m)+z(m),where {tilde over (H)}(m) represents the actual channel characteristicsused in channel 160 and H(m)={tilde over (H)}(m)P(m)=[h₁[m], . . . ,h_(S)[m]] is a R×S matrix representing the effective transmissionchannel as modified by precoder 150. In some embodiments, the precodingmatrix P can be chosen such that an effective transmission channel H(m)is created that maximizes the diversity gain of the system. For example,precoding matrix P may be chosen to change the apparent characteristicsof the channel so that the effective channel matrix is more orthogonalthan the actual channel matrix. Precoding matrix P may be a Givensrotation matrix, a Vandermonde matrix, a Fourier matrix, a Hadamardmatrix or another type of matrix.

Each codeword, e.g., codeword 112, may be mapped to a set of streamsx_(i) ₁ [m] through x_(i) _(c(i)) [m] (where c(i) denotes the totalnumber of streams corresponding to codeword i, 1≦c(i)≦S). In otherwords, an i^(th) codeword (equivalently codeword i, or codewordassociated with index i) may be associated with a set of stream indicesS_(i)={i₁, i₂, . . . , i_(c(i))}, such that the stream vector x_(i)(m)coming from the i^(th) codeword is expressed as:x _(i)(m)=[x _(i) ₁ [m], . . . ,x _(i) _(c(i)) [m]] ^(T).Similarly, the channels corresponding to the i^(th) codeword(alternatively codeword i) may be expressed using stream indices fromthe stream set S_(i) as follows:H _(i)(m)=[h _(i) ₁ [m], . . . ,h _(i) _(c(i)) [m]].

One technique for improving the performance of a MIMO system, e.g., ofsystem 100 of FIG. 1, is to use successive interference cancellation(SIC). According to this technique, a codeword associated with strongchannel conditions may be decoded first before other codewordsassociated with weaker channel conditions. Effects of this decodedcodeword may be subtracted from a received signal vector, e.g., receivedvector y of FIG. 1, to eliminate interference of the decoded codeword onthe other codewords.

SIC can be implemented in a number of ways. For example, SIC can beimplemented such that all codewords are decoded in parallel, such thatone codeword is serially decoded at each stage, or such that anyarbitrary number of codewords is decoded simultaneously at each stage.

Techniques for implementing SIC are described, for example, inco-pending, commonly-assigned U.S. patent application Ser. No.13/047,056, entitled “MULTIPLE-INPUT MULTIPLE-OUTPUT RECEIVERS USINGSUCCESSIVE INTERFERENCE CANCELLATION BASED ON CYCLE REDUNDANCY CHECK”,filed Apr. 2, 2010, which is hereby incorporated by reference herein inits entirety. Hereafter, and for the purposes of illustration, thisdisclosure will primarily discuss the serial SIC implementation. Thesystems and methods of this disclosure, however, may apply to otherimplementations of SIC. For example, any SIC implementation (e.g.,serial or parallel) may use the SIC ordering generated using thetechniques described herein.

FIG. 2 illustrates a block diagram of system 200 for performing SICaccording to some embodiments. System 200 may include C decoding stages1, 2, . . . , C for respectively decoding codewords L₁, L₂, . . . ,L_(C). For ease of illustration, only 4 of the stages are shown, and thestages are chosen to correspond, respectively, to codewords 1, 2, . . ., C in that order (i.e., L₁=1, L₂=2, . . . , L_(C)=C). However, anydesired ordering of codewords may be used, as will be explained infurther detail below.

Each stage (except the last stage) may include a receiver block for acodeword i and an interference canceller associated with that codewordi. For example, receiver block 202 and interference canceller 204 maycorrespond to the first stage associated with codeword 1, receiver block206 and interference canceller 208 may correspond to the second stageassociated with codeword 2, and receiver block 256 and interferencecanceller 258 may correspond to the C−1^(th) stage associated withcodeword C−1. The last stage may include a receiver block associatedwith codeword C and no interference canceller block, for example,receiver block 260. Although the receiver and interference cancellerblocks of FIG. 2 are illustrated as separate blocks, the blocks may beimplemented as one or multiple components by a single or multipleintegrated circuit boards or devices.

At the first stage, receiver 202 may decode codeword 1 based on areceived signal vector y, e.g., received signal vector y from FIG. 1, togenerate decoded codeword 1. Interference canceller 204 may receivereceived signal vector y as well as the decoded codeword 1, as output byreceiver 202. Interference canceller 204 may generate aninterference-free received signal vector ŷ₂, where the interference dueto codeword 1 is cancelled.

At the second stage, receiver 206 may decode codeword 2 based on theinterference-free received signal vector ŷ₂ to generate decoded codeword2. Interference canceller 208 may also receive ŷ₂ from interferencecanceller 204 as well as the decoded codeword 2 output by receiver 206.Interference canceller 208 may generate an interference-free receivedsignal vector ŷ₃, where the interferences due to codewords 1 and 2 arecancelled by removing the effect of codeword 2 from theinterference-free received signal vector ŷ₂.

Similarly to the second stage, a receiver block at an SIC stage i (i=3,. . . , C−1) may decode a codeword i based on an interference-freereceived signal vector ŷ_(i). An interference canceller may output aninterference-free received signal vector ŷ_(i+1), where theinterferences due to codewords 1 through i are cancelled.

At the last stage, receiver block 260 may decode codeword C based oninterference-free received signal vector ŷ_(C) as output by interferencecanceller 258 of stage C−1.

One example of a SIC process that can be implemented in receiver andinterference canceller blocks of FIG. 2 is illustrated in FIG. 3. FIG. 3is a flow diagram illustrating process 300 for performing SIC using anexemplary codeword ordering (L₁, L₂, . . . , L_(C)). Process 300includes 302, 304, 306, 308, 310, 312, 314, and 316. In someembodiments, 310 may be implemented in receiver blocks of FIGS. 2 and312, 314, and 316 may be implemented in interference canceller blocks ofFIG. 2.

At 302, an ordering for decoding codewords in respective stages of theSIC process is determined. This ordering may be representedmathematically as follows:{1,2, . . . ,C}→Π=(L ₁ ,L ₂ ,L _(C))  (EQ. 3)In other words, the C codewords (e.g., codewords 101 through 105 ofFIG. 1) are mapped to an ordered C-tuple or ordering Π. The tuple Πdefines the order in which the codewords may be decoded at each SICstage i (i=1, . . . , C). In the example illustrated in FIG. 2,codewords 1 through C were decoded in that order (i.e., L_(i)=i).However, any suitable ordering Π may be used. Techniques for determiningsuch an ordering will be discussed in further detail below.

At 304, parameters for process 300 may be initialized. For example, theinterference-free received signal vector ŷ₁ used by the first stage(e.g., received by the interference cancellation block 204 of FIG. 2)may be initialized to the received signal vector y. In addition, theeffective channel matrix Ĥ₁ for the first stage (e.g., used by thereceiver 202 and/or interference canceller 204 of FIG. 2) may beinitialized to the effective channel matrix H (e.g., effective channel130 of FIG. 1).

At 306, it is determined whether all C codewords have been decoded. Ifall codewords have been decoded (i.e., i>C), process 300 may beterminated. Otherwise, if there is any codeword still undecoded (i.e.,1≦i≦C), the codeword may be decoded using 308, 310, 312, 314, and 316.Each iteration for decoding one codeword may correspond to one SICstage.

At 308, a codeword to be decoded at SIC stage i is selected. Forexample, the codeword i to be decoded at SIC stage i may be determinedto be L_(i), as defined by the ordering generated at 302.

At 310, the codeword selected at 308, e.g., the L_(i) ^(th) codeword,may be demodulated and/or decoded based on the interference-free signalreceived at the i^(th) stage, ŷ_(i) and the SIC-adjusted effectivechannel matrix at the i^(th) stage, Ĥ_(i). This effective channel matrixcorresponds to the composite channels for the codewords that have notbeen decoded at the i−1^(th) stage, i.e., Ĥ_(i)=[H_(L) _(i) , . . .,H_(L) _(C) ]. The specific demodulation and decoding methods used at310 may depend on the receiver implementation.

At 312, the transmitted signal vector is re-encoded and modulated togenerate reconstructed transmitted signal vector {circumflex over(x)}_(L) _(i) that takes into account the interference cancellation dueto the L_(i) ^(th) codeword.

At 314, the interference-free received signal vector ŷ_(i+1) for stagei+1 is generated by cancelling the interference from theinterference-free received signal vector ŷ_(i) for stage i. This may bedone by subtracting the effect of the reconstructed transmitted signalvector {circumflex over (x)}_(L) _(i) (i.e., as modified by the channelsH_(L) _(i) (m) corresponding to codeword L_(i)) from theinterference-free received signal vector ŷ_(i):ŷ _(i+1)(m)=ŷ _(i)(m)−H _(L) _(i) (m){circumflex over (x)} _(L) _(i) (m)

At 316, the effective channel for the i+1^(th) stage, Ĥ_(i+1) may begenerated. For example, Ĥ_(i+1) may be generated by deleting the columnscorresponding to the channels for the L_(i) ^(th) codeword as follows:Ĥ _(i+1)(m)=Ĥ _(i)(m)\H _(L) _(i) (m),where A\B denotes the deletion of columns of the matrix B from thematrix A.

Next, 306 may be checked again. The next codeword, L_(i+1), may bedecoded next (i.e., i is incremented to i+1), unless all codewords havebeen decoded, in which case process 300 may be terminated.

Although process 300 describes SIC using iterative stages, this is meantto be illustrative and is not meant to be limiting or exhaustive. Themethods and processes described herein apply to other representations ofSIC, including QR representation.

Returning to the ordering determination 302 of process 300 of FIG. 3above, various techniques may be used to determine the ordering in whichcodewords are decoded in each one of the SIC stages. In someembodiments, a locally optimal ordering is determined based on thechannel quality associated with each codeword. Various metrics, such asthe Minimum Mean Square Error (MMSE) criterion or the Zero Forcing (ZF)criterion, may be used to determine a channel quality for each codeword.For example, a performance component γ_(i) based on channel quality maybe computed for each codeword i. An example of such a performancecomponent may be computed using an average channel gain associated witha codeword i as follows:

$\gamma_{i} = {\frac{1}{M{S_{i}}}{\sum\limits_{s \in S_{i}}{\sum\limits_{m = 1}^{M}{{h_{s}(m)}}^{2}}}}$where ∥h_(s)∥ is the norm of the channel corresponding to a stream s inthe stream set S_(i) of codeword i. Channel norms ∥h_(s)∥ are averagedover all streams corresponding to the codeword i (i.e., all streams s instream set S_(i)) and over all received coded symbol vectors m (i.e.,m=1, . . . , M) to compute the performance component γ_(i). A locallyoptimal codeword may be chosen that maximizes the selected performancecomponent. For example, the codeword to be decoded during the first SICstage, L₁ is determined to be the codeword that has the maximum channelgain:L ₁=arg max_(1<i≦C)γ_(i)  (EQ. 4)

To determine the subsequent codewords to be decoded in subsequent SICstages (i.e., L₂, L₃, . . . , L_(C)), the same process for determiningthe first codeword L₁ above may be repeated for the remaining C-1codewords. The metrics or criteria used to compute the channel qualityfor each one of these remaining codewords may be adjusted given that theinterference from the first codeword has been cancelled.

The codeword selected at each stage in this local optimal ordering(e.g., using EQ. 4) only considers the performance components ofcodewords left undecoded at that particular stage. At each stage,decoding the locally optimal codeword may or may not maximize theinterference cancellation of the codewords decoded in other SIC stages,and may assume perfect interference cancellation, which may not alwaysbe true. Another consideration is that SIC results in error propagation,because if a codeword decoded at a specific SIC stage is not recoverederror-free, then errors may propagate to the interference cancellationof subsequent SIC stages, thereby degrading performance. Even forerror-aware orderings (e.g., that use criteria that take into accounterror propagation between SIC stages), error compensation in a locallyoptimal ordering (e.g., using EQ. 4) may only be done at the next stageof ordering and decoding.

The locally optimal codeword to decode at a particular SIC stage may notguarantee the largest interference cancellation gain for all codewords,i.e., a global optimum. For example, it may be globally optimal to firstdecode a codeword that is not locally optimal at that stage. This isillustrated in the following illustrative example. Three codewords 1, 2,and 3 with respective coding rates R₁=½, R₂=½, and R₃=¼ are eachtransmitted in a MIMO system with a noise variance σ²=0.2. Therespective effective channels associated with each codeword are:

${h_{1} = \begin{bmatrix}1 \\1 \\0\end{bmatrix}};{h_{2} = \begin{bmatrix}0 \\0 \\\sqrt{2}\end{bmatrix}};{h_{3} = {\sqrt{\frac{2}{3}}\begin{bmatrix}1 \\1 \\1\end{bmatrix}}}$In this example, the locally optimal ordering based on an MMSE criterioncan be determined to be Π₁=(2, 1, 3). This locally optimal orderingachieves a resulting SNR of [SNR⁽¹⁾=1.11, SNR⁽²⁾=1.36, SNR⁽³⁾=2] forcodewords 1, 2, and 3, respectively. A different ordering Π₂=(3, 1, 2),however, achieves a better global resulting SNR, [SNR⁽¹⁾=2, SNR⁽²⁾=2,SNR⁽³⁾=0.67]. While the interference gain of codeword 3 in ordering Π₂is lower than in ordering Π₁, it is still possible to decode codeword 3successfully because of its low coding rate (R₃=¼). Compared to Π₁,ordering Π₂ achieves higher SNR and interference cancellation gainvalues for codewords 1 and 2, which have a higher coding rate thancodeword 3 (R₁=R₂=½). Therefore, the locally suboptimal ordering Π₂achieves a better overall resulting SNR than the locally optimalordering Π₁. This example illustrates that determining a globallyoptimal ordering may need to consider all possible orderings, and notonly the locally optimal codeword at each SIC stage.

FIG. 4 illustrates a process for determining a globally optimal SICordering for codewords in accordance with some embodiments. Process 400includes 402, 404, 406, 408, and 410 and may be implemented inprocessing circuitry of receiver 180 of FIG. 1 or receiver/interferencecancellation blocks of FIG. 2.

At 402, a plurality of K orderings of codewords 1 through C isdetermined. In some embodiments, all possible orderings (i.e.,K=C!=C×(C−1)× . . . ×1) may be determined. In some embodiments, a subsetof all possible orderings may be determined.

At 404, one ordering from the determined plurality of K orderings isselected. This ordering may be represented as:Π^((k))=(L ₁ ^((k)) ,L ₂ ^((k)) , . . . ,L _(C) ^((k))),where k denotes the selected ordering (k=1, . . . , K), and L_(i) ^((k))(i=1, . . . , C) is the i^(th) codeword to decode in that particularordering Π^((k)).

At 406, a performance component γ_(i) is computed for each codeword i inthe ordering Π^((k)) selected at 404. This performance component may bebased on packet error rate (PER), equalizer-outputSignal-to-Interference and Noise Ratio (SINR), channel gain, achievabledata-rate, and/or any other suitable performance metric.

At 408, the ordering selection at 404 and performance computation at 406may be repeated for other orderings from the plurality of orderingsdetermined at 402. In this way, each one of the plurality of orderingsdetermined at 402 may be associated with C performance components foreach one of the C codewords in that ordering.

At 410, a globally optimal ordering from the plurality of orderingsdetermined at 402 is determined. This ordering may be selected tomaximize a particular objective function G, such as total throughput ortotal probability of decoding for codewords arranged in a correspondingordering. The objective function may be predetermined or may bedynamically determined based on receiver implementation, channelconditions, and/or any suitable criterion. The objective function may becomputed for each ordering based on the performance components of the Ccodewords in that ordering, i.e., G(γ_(i)(Π^((k))); 1≦i≦C). The globallyoptimal Π_(opt) may be determined as follows:

$\begin{matrix}{{\prod\limits_{opt}\;{= {\underset{\prod^{(k)}{,{1 \leq k \leq {C!}}}}{\arg\;\max}{G\left( {{\gamma_{i}\left( \prod^{(k)} \right)};{1 \leq i \leq C}} \right)}}}},} & \left( {{EQ}.\mspace{14mu} 5} \right)\end{matrix}$where Π_(opt) is the ordering Π^((k)) that maximizes the objectivefunction G over all C! orderings. Because Π_(opt) considers performancecomponents of codewords as arranged in all possible orderings, Π_(opt)corresponds to a global optimum.

Any appropriate objective function G may be used in EQ. 5. For example,objective function G for a particular ordering may correspond to a totalthroughput achieved by the corresponding ordering. Objective function Gmay also measure a total decoding probability, a totalSignal-to-Interference and Noise Ratio (SINR), and/or a total channelgain for the codewords in each ordering. Objective function G maymeasure a minimum performance metric for one codeword among all thecodewords in each ordering. For example, objective function G maycorrespond to a minimal throughput for a codeword among all codewords inthe corresponding ordering, a lowest decoding probability for a codewordamong all codewords in the ordering, a minimal SINR for a codeword amongall codewords in the corresponding ordering, and/or a minimal channelgain for a codeword among all codewords in the corresponding ordering.These objective functions are merely illustrative, and are not meant tobe limiting or exhaustive in any way. Any appropriate objective functionmay be chosen without departing from the scope of the disclosure.

In some embodiments, a performance component of a codeword may becomputed using a probability that an error occurred in the receivedcodeword. This probability may be evaluated using a packet error rate(PER), which may be calculated as the number of erroneously receiveddata packets divided by the total number of received packets.Performance components based on PER may be computed for each one of theC codewords in each one of the C! possible ordering, to determine theglobally optimal ordering, e.g., as described in process 400 above.However, this may be prohibitively expensive in terms of complexityand/or time. In some embodiments, instead of decoding all codewords foreach ordering and computing performance components (e.g., collectingPER) for each decoded codeword and each ordering, performance componentsmay be predicted from the channel information. An exemplaryimplementation of this is shown in FIG. 5.

FIG. 5 is a flow diagram illustrating process 500 to determine a SICordering using performance components predicted from channelinformation, in accordance with embodiments of the present disclosure.Process 500 includes 502, 504, 506, 508, 510, 512, and 514, and may beimplemented in processing circuitry of receiver 180 of FIG. 1 orreceiver/interference cancellation blocks of FIG. 2.

At 502, a plurality of possible orderings K is determined. This may bedone similarly to 402 of FIG. 4 above.

At 504, one ordering is selected from the possible orderings determinedat 502. For purposes of illustration, exemplary ordering Π⁽¹⁾=(1, 2, . .. , C) will be used in the description of 504, 506, 508, and 510 below.This is not meant to be limiting or exhaustive in any way, and generallyany ordering Π^((k)) may be selected.

At 506, and for each codeword i in the ordering selected at 504, aneffective channel quality Ĥ_(i) associated with the codeword i is mappedto an effective stream SNR_(S) value for each stream s that correspondsto the codeword i. This may be represented as follows:{H _(i)(m),σ² ,E _(i) }→SNR _(s)(m);1≦m·M,sεS _(i),where SNR_(S)(m) is the effective stream SNR_(S) value per coded symbolm for a stream s in the stream set S_(i) corresponding to codeword i,Ĥ_(i)(m) is the effective channel at the i^(th) stage (i.e., afterdeleting the channels of the previously decoded codewords as describedat 316 of FIG. 3 above), σ² is the variance associated with thetransmission channel, and E_(i) is the accumulated error propagation atthe i^(th) stage from pervious SIC stages. The particular mapping fromchannel quality to effective SNR depends on the type of equalizer (e.g.,MMSE), the error compensation method, and/or the mapping method.Examples of the mapping method include, but are not limited to, finitealphabet capacity (FAC), mean mutual information per bit (MMIB), andexponential effective SNR mapping (EESM).

At 508, and for each codeword i (1≦i≦C), the effective stream SNR_(s)(m)values computed at 506 are combined over all streams corresponding tocodeword i and over all coded symbols to generate an effective codewordSNR^((i)) value. For example, the effective stream SNR_(S)(m) values maybe averaged over all coded symbols m (i.e., m=1, . . . , M) and over allstreams corresponding to codeword i (i.e., all streams s in stream setS_(i)) as follows:

${{SNR}^{(i)} = {\frac{1}{M{S_{i}}}{\sum\limits_{s \in S_{i}}{\sum\limits_{m = 1}^{M}{{SNR}_{s}(m)}}}}};{1 \leq i \leq C}$

At 510, the effective codeword SNR^((i)) value is mapped to a predictedPER using pre-collected statistics for the corresponding coding rate.For example, look-up tables mapping various ranges of codeword SNR^((i))values to PER_(i) values for different coding rates may be used. Thismay be represented as:PER _(i) =f _(Coding-Rate)(SNR ^((i))),where f_(Coding-Rate) corresponds to the PER mapping function used inthe receiver implementation.

At 512, the ordering selection at 504, effective channel quality mappingat 506, effective codeword SNR generation at 508, and codeword PER_(i)computation at 510 may be repeated for each codeword i (1≦i≦C) in eachone of the plurality of possible orderings determined at 502.

At 514, an optimal ordering Π_(opt) is determined that maximizes anobjective function G over all possible orderings Π^((k)) (1≦k≦K). Thismay be done using EQ. 5, by setting performance component γ_(i) of eachcodeword i in each ordering Π^((k)) to the PER_(i) value correspondingto the codeword i in that ordering (i.e.,γ_(i)(Π^((k)))=PER_(i)(Π^((k))) for 1≦k≦K and 1≦i≦C).

For each ordering Π^((k)), the objective function G may consider thejoint performance of codewords arranged in that particular ordering. Oneexemplary objective function may include overall decoding probabilityfor a particular ordering. For example, for a given ordering Π, theobjective function G may be the sum of the probability of successfultransmission (1-PER_(i)) of all codewords i, each weighted by the datarate R_(T) associated with the respective codeword i, as follows:

${G\left( {\gamma_{i}(\prod)} \right)} = {{\sum\limits_{i = 1}^{C}{R_{i} \times \left( {1 - {\gamma_{i}(\prod)}} \right)}} = {\sum\limits_{i = 1}^{C}{R_{i} \times \left( {1 - {{PER}_{i}(\prod)}} \right)}}}$Another exemplary objective function may measure total throughput. Forexample, for a given ordering Π, the objective function G may be theprobability that all C codewords were successfully transmitted, i.e.:G(γ_(i)(Π))=Π_(i=1) ^(C)(1-γ_(i)(Π))=Π_(i=1) ^(C)(1-PER _(i)(Π))These objective functions are merely illustrative and are not meant tobe exhaustive or limiting. For example, objective function G may measurea total Signal-to-Interference and Noise Ratio (SINR), channel gain,and/or any suitable performance metric for the codewords in eachordering. Objective function G may measure a minimal throughput,decoding probability, channel gain, SINR, and/or any suitableperformance metric for one codeword among all codewords in thecorresponding ordering. Any appropriate objective function may be chosenwithout departing from the scope of the disclosure.

As illustrated in FIGS. 4 and 5 above, performance components(γ_(i)(Π^((k))) may be computed for each codeword i (1≦i≦C) in each oneof all possible orderings (1≦k≦C!). Since each ordering may require Cperformance components (for each one of the C codewords), a total ofC×C! performance components may need to be computed. For example, in thecase of 4 codewords, there may be a total of 4!=24 orderings to considerand 4×4!=96 performance components to compute. Techniques for reducingcomplexity may be employed to increase efficiency of determining theglobally optimal SIC ordering without needing to compute all C×C!performance components.

One such complexity reduction technique is based on recognizing that asame performance component may be reused for different orderings. Forexample, a performance component γ₁ for codeword 1 may be the same forall orderings that begin with codeword 1, e.g., Π=(1, 2, 3, 4) and Π=(1,2, 4, 3). Therefore, this performance component may be computed onlyonce and may used for all orderings that begin with codeword 1.Therefore, in some embodiments, a performance component may be computedonce for each particular one of the C codewords that may be decodedfirst. These performance components may be used for all orderings thatbegin with that particular codeword. For example, these performancecomponents may be saved, e.g., in a storage area of the receiver.Similarly, a performance component may be computed once for eachparticular one of the C×(C−1) possible codewords to be decoded secondgiven a first decoded codeword L₁. These performance components may beused for all orderings that begin with codeword L₁ followed by theparticular codeword. Generally, the performance components can becomputed, in a recursive fashion, for each particular one of theC×(C−1)× . . . ×(C−k+1) codewords to be decoded as the k^(th) codewordgiven the first k−1 decoded codewords. These performance components maybe stored in a storage area of the receiver for use by all orderingsthat begin with the same k codewords. This low complexity performancecomponents computation can be represented, but not limited to, a tree ofcomponents as explained below. Other representations, such as trellis,can also be applied in some embodiments. The low complexity performancecomponents computation can be implemented serially as described herein,or may be executed or performed in parallel where appropriate to reducelatency and processing times.

A tree of performance components may be used to represent uniqueperformance components for all codewords in all possible orderings. Inthis tree, each node (other than the root node) may represent oneperformance component for a codeword i in a specific position k (k=1, .. . , C) in all possible orderings.

FIG. 6A is a flow diagram illustrating process 680 for generating a treeof performance components in a SIC process in accordance withembodiments of the present disclosure. In some embodiments, process 680may be employed as part of 406 of process 400 of FIG. 4 or 510 ofprocess 500 of FIG. 5. In some embodiments, process 680 may be performedprior to, or in parallel with, process 400 of FIG. 4 or process 500 ofFIG. 5. Process 680 may include 682, 684, and 686, and be implemented inprocessing circuitry of receiver 180 of FIG. 1 or receiver/interferencecancellation blocks of FIG. 2.

FIG. 6B is an exemplary tree 600 of performance components that may havebeen generated using process 680 of FIG. 6A for the illustrative case ofC=4. Each node represents a performance component γ₁ to compute for acodeword i at the k^(th) tree layer, or depth-k. This k^(th) tree layer,or depth-k, represents the k^(th) position in a SIC ordering, orequivalently, the k^(th) SIC stage in which codeword i is decoded. Thenumber shown next to each node denotes the associated codeword i.Although process 680 will be described below using tree 600, this ismerely exemplary and is not meant to be limiting or exhaustive.

At 682, the tree is initialized by generating a root node. For example,root node 601 of tree 600 may be generated at 682. This root node mayrepresent no performance component.

At 684, C children nodes of the root node are generated as the firstlayer of the tree (i.e., at depth-1). Each node corresponds to arespective one of the C codewords, and represents a performancecomponent γ₁ for a codeword i in all possible orderings that begin withthis codeword.

In the exemplary tree 600, C=4 codewords 602, 603, 604, and 605 aregenerated at depth-1 of the tree. Node 602, for example, represents aperformance component γ₁ for codeword 1 in all orderings Π that beginwith codeword 1.

At 686, other nodes of the tree may be generated as follows: For eachnode N at depth-k (k=1, . . . , C), C−k children nodes are generated,where each child node is associated with a codeword that is not alreadyrepresented by the nodes along the path from the root node to node N. Inother words, if the nodes on the path from the root node to the node Nat depth-k correspond to codewords L₁<L₂< . . . <L_(k) in that order,then the children of this node N are selected from the set of codewordsother than the codewords associated with nodes on the path to the rootnode (i.e., other than L₁, L₂, . . . , L_(k)). This set of codewordsfrom which the children nodes of the node N may be generated can berepresented as {1, . . . , C}\{L₁, L₂, . . . , L_(k)}, where A\Brepresents set A without the elements of set B.

In the exemplary tree 600, nodes at depth-2, depth-3, and depth-4 may begenerated using 686. For example, for node 602 at depth-1, C−k=4−1=3children nodes are generated from set {1, . . . , C}\{L₁, L₂, . . . ,L_(k)}={1, 2, 3, 4}\{1}={2, 3, 4}.

The total number of performance components to compute using a tree asillustrated in process 680 of FIG. 6A above is equal to the number ofnodes of the generated tree, i.e.,

$\sum\limits_{k = 0}^{C - 1}{\frac{C!}{k!}.}$This represents a significant reduction relative to computing C×C!performance components in a process that computes C performancecomponents for each one of C! orderings. For example, in the caseillustrated in FIG. 6B, tree 600 has a total of 4+4×3+4×3×2+4×3×2×1=64nodes, representing 64 performance components to compute. Thiscorresponds to ⅔ of the total number of performance components (i.e., a⅓ complexity reduction) compared to the process that computes allC×C!=96 performance components.

The complexity of computing performance components may be furtherreduced based on the following observation: in some systems, the errorpropagation in a SIC process from cancelling the interferences ofcodeword m then codeword n is the same as that from cancelling theinterferences of codeword n and then codeword m. In other words, theordering of codewords in a SIC process may be irrelevant to the errorpropagation between SIC stages. In its simplest form, the assumption ofperfect cancellation implies this order independence of errorpropagation because it assumes that no error is propagated between SICdecoding stages.

In some embodiments, the order independence of error propagation may beapplied in the following way: given a partition of codewords to decodebefore a codeword i in an ordering Π, the performance component of thiscodeword i depends on what codewords are in the partition of codewordsbefore codeword i (equivalently, in the partition of codewords aftercodeword i), but not on the particular order of codewords within thesepartitions. In other words, given two different orderings Π_(m) andΠ_(n), where a codeword i has the same position k in each ordering, andwhere the partitions before and after codeword i are respectively thesame, the performance components of codeword i in the two orderings areidentical. This may be represented mathematically as:Π_(m)=(L ₁ ^((m)) . . . L _(k−1) ^((m)) iL _(k+1) ^((m)) . . . L _(C)^((m)))Π_(n)=(L ₁ ^((n)) . . . L _(k−1) ^((n)) iL _(k+1) ^((n)) . . . L _(C)^((n)))γ_(i)(Π_(m))=γ_(i)(Π_(n))

{L ₁ ^((m)) . . . L _(k−1) ^((m)) }={L ₁ ^((n)) . . . L _(k−1) ^((n))},where { } corresponds to a set (irrespective of order of the elements inthe set) and ( ) corresponds to a tuple (defining the particular orderof elements in the corresponding ordering). Codeword i has the sameperformance components in both orderings (i.e.,γ_(i)(Π_(m))=γ_(i)(Π_(n))) if and only if the set of k−1 elements thatprecede codeword i in ordering Π_(m) is the same as the set of k−1elements that precede codeword i in ordering Π_(n) (irrespective oforder). This set of k−1 elements preceding a particular codeword i in anordering will be referred to herein as a partition of precedingcodewords correspond to codeword i in that ordering.

Using this observation of order-independent error propagation, fewerperformance components may need to be computed. For example, the treegeneration techniques from FIGS. 6A and 6B above may be modified to onlycompute performance components for codewords having unique partitions ofpreceding codewords. The tree representation of the performancecomputation is only for example and not limiting. Other representations,such as trellis, can also be applied in some embodiments. Although thelow complexity performance components computation is described aboveusing a serial implementation, this process may be executed or performedsubstantially simultaneously where appropriate or in parallel to reducelatency and processing times.

FIG. 7A is a flow diagram illustrating process 780 for generating a treeof performance components in a SIC process in accordance withembodiments of the present disclosure. In some embodiments, process 780may be employed as part of 406 of process 400 of FIG. 4 or 510 ofprocess 500 of FIG. 5. In some embodiments, process 680 may be performedprior to, or in parallel with, process 400 of FIG. 4 or process 500 ofFIG. 5. Process 780 may include 782, 784, 7786, 788, and 790, and beimplemented in processing circuitry of receiver 180 of FIG. 1 orreceiver/interference cancellation blocks of FIG. 2.

FIG. 7B is an exemplary tree 700 of performance components that may havebeen generated using process 780 of FIG. 7A for the illustrative case ofC=4. Each node represents a performance component γ₁ to compute for acodeword i at the k^(th) tree layer, or depth-k (corresponding to theposition of the codeword i in the ordering). The number shown next toeach node denotes the associated codeword i. Although process 780 willbe described below using tree 700 of FIG. 7, this is merely exemplaryand is not meant to be limiting or exhaustive.

At 782, the tree is initialized by generating a root node. For example,root node 701 of tree 700 may be generated at 702. This root node mayrepresent no performance component.

At 784, C children nodes of the root node are generated as the firstlayer of the tree (i.e., at depth-1). Each node corresponds to arespective one of the C codewords, and represents a performancecomponent γ₁ for a codeword i in all possible orderings that begin withthis codeword.

In the exemplary tree 700, C=4 codewords 702, 703, 704, and 705 aregenerated at depth-1 of the tree. Node 702, for example, represents aperformance component γ₁ for codeword 1 in all orderings Π that beginwith codeword 1.

At 786, the rest of the tree may be generated as follows: For each nodeN associated with a codeword i at depth-k (k=1, . . . , C), it isdetermined whether the parent node represents a performance componentfor a codeword j such that j is larger than i (j>i). If the parent isassociated with a codeword j>i, then no children nodes are generated fornode N at 788. Otherwise, if the parent is associated with a codewordj≦i, then children nodes are generated for node N according to 790. Itshould be noted that the condition of 786 (i.e., j>i) is merelyexemplary and is not intended to be exhaustive or limiting in any way.Other conditions may be used such as performance components are computedfor codewords having unique partitions of preceding codewords.

At 790, C−k children nodes are generated for node N, where each childnode is associated with a codeword that is not already represented bythe nodes along the path from node N to the root node. In other words,if the nodes on the path from the root node to the node N at depth-kcorrespond to codewords L₁<L₂< . . . <L_(k) in that order, then thechildren of this node N are selected from the set of codewords otherthan the codewords associated with nodes on the path to the root node(i.e., other than L₁, L₂, . . . , L_(k)). This set of codewords fromwhich the children nodes of the node N may be generated can berepresented as {1, . . . , C}\{L₁, L₂, . . . , L_(k)}, where A\Brepresents set A without the elements of set B. The condition of 786thus ensures that children nodes are only generated for a codeword witha unique partition of codewords preceding it in the path to the rootnode.

In the exemplary tree 700, nodes at depth-2, depth-3, and depth-4 may begenerated using 786. For example, for node 702 at depth-1, C−k=4−1=3children nodes are generated from set {1, . . . , C}\{L₁, L₂, . . . ,L_(k)}={1, 2, 3, 4}\{1}={2, 3, 4}.

At depth-2, node 707 corresponds to codeword 3 and its parent node 702corresponds to codeword 1. Since node 707 corresponds to a codeword thathas a higher index than that of its parent node (i.e., 3>1), thenC−k=4−2=2 children nodes are generated for node 707 from set {1, 2, 3,4}\{1, 3}={1, 2}.

At depth-3, node 720 corresponds to codeword 2 and its parent node 707corresponds to codeword 3. Since node 720 corresponds to a codeword thathas a lower index than that of its parent node (i.e., 2<1), then nochildren nodes are generated for 720.

The total number of performance components to compute using a tree asillustrated in process 780 of FIG. 7A above is equal to the number ofnodes of the generated tree, i.e., C×2^(c−1). This represents asignificant reduction relative to independently computing C×C!performance components or to computing

$\sum\limits_{k = 0}^{C - 1}\frac{C!}{k!}$using process 680 of FIG. 6A above. For example, in the case illustratedin FIG. 7B, tree 700 has a total of 4+12+12+4=32 nodes corresponding to32 performance components to compute. This is equal to ⅓ of the totalnumber of performance components (i.e., represents a ⅔ complexityreduction) compared to the process that computes all C×C!=96 performancecomponents.

The above steps of the flowcharts of FIGS. 3, 4, 5, 6A, and 7B maygenerally be executed or performed in any order or sequence not limitedto the order and sequence shown and described in the figure. Also, someof the above steps of processes 300, 400, 500, 680, and 780 may beexecuted or performed substantially simultaneously where appropriate orin parallel to reduce latency and processing times. Any of the steps inthese processes may be omitted, modified, combined, and/or rearranged,and any additional steps may be performed, without departing from thescope of the present disclosure.

The foregoing describes systems and methods for reliable and efficientinformation transmission. Those skilled in the art will appreciate thatthe disclosed methods and systems can be practiced by other than thedescribed embodiments, which are presented for the purpose ofillustration rather than of limitation. Modifications and variations arepossible in light of the above teachings or may be acquired frompractice of the disclosed methods and systems. While certain componentsof this disclosure have been described as implemented in hardware andothers in software, other configurations may be possible.

What is claimed is:
 1. A method for determining a successiveinterference cancellation (SIC) ordering for a plurality of codewords,the method comprising: for each ordering of a plurality of SIC orderingsof the codewords, mapping an effective channel quality associated witheach codeword to a Packet Error Rate (PER) value associated with theeach codeword, wherein said mapping is based on a position of the eachcodeword in the each ordering; and selecting, using decoder circuitry,one ordering from the plurality of SIC orderings for decoding thecodewords using SIC, wherein said selecting is based on a performanceobjective computed from the PER value associated with the each codeword.2. The method of claim 1, further comprising: receiving the plurality ofcodewords; identifying the plurality of SIC orderings for the receivedcodewords, wherein each ordering defines a unique order of the receivedcodewords for decoding using SIC; and after receiving the plurality ofcodewords, performing said mapping the effective channel quality for theeach ordering based on the identified plurality of SIC orderings.
 3. Themethod of claim 1, wherein said mapping comprises computing one of thegroup consisting of a predicted Packet Error Rate (PER) value, aSignal-to-Interference and Noise Ratio (SINR) value, a channel gain, andan achievable data rate for the each codeword.
 4. The method of claim 1,wherein said mapping comprises: mapping the effective channel qualityassociated with the each codeword to an effective stream Signal-to-Noiseratio (SNR) value for each stream of a plurality of streams associatedwith the each codeword; combining effective stream SNR values for theplurality of streams associated with the each codeword to generate aneffective codeword SNR value for the each codeword; and mapping theeffective codeword SNR value to the performance objective associatedwith the each codeword.
 5. The method of claim 1, further comprisingcomputing a unique performance component for the each codeword at aparticular position in the each ordering.
 6. The method of claim 5,further comprising generating a tree of nodes arranged in a plurality oflayers associated with respective SIC stages, wherein each node at aparticular layer represents the unique performance component for acodeword to be decoded in a SIC stage associated with the particularlayer.
 7. The method of claim 6, further comprising: generating a firstlayer of the tree, wherein the first layer comprises a plurality ofnodes associated with respective ones of the codewords; and generating achild node of the node N, wherein a path from a root node of the tree tothe node N comprises at least one path node, and wherein the child nodeis associated with a codeword other than at least one codewordassociated with the at least one path node.
 8. The method of claim 5,wherein said computing a unique performance component comprisescomputing a performance component for a codeword in the each ordering inresponse to determining that the codeword is preceded by a uniquepartition of codewords in the each ordering.
 9. The method of claim 8,further comprising: generating a first layer of the tree, wherein thefirst layer comprises a plurality of nodes associated with respectiveones of the codewords; for a node N of the tree associated with acodeword i, determining if a parent node of the node N is associatedwith a codeword j where j is larger than i; and in response todetermining that the parent node of node N is associated with a codewordj where j is larger than i, generating a child node of the node N,wherein a path from a root node of the tree to the node N comprises atleast one path node, and wherein the child node is associated with acodeword other than at least one codeword associated with the at leastone path node.
 10. The method of claim 1, wherein the performanceobjective is based on at least one selected from the group consistingof: a total throughput for the codewords in the each ordering; a totaldecoding probability for the codewords in the each ordering; a totalSignal-to-Interference and Noise Ratio (SINR) for the codewords in theeach ordering; a total channel gain for the codewords in the eachordering; a minimal throughput for a codeword among all codewords in theeach ordering; a lowest decoding probability for a codeword among allcodewords in the each ordering; a minimal SINR for a codeword among allcodewords in the each ordering; and a minimal channel gain for acodeword among all codewords in the each ordering.
 11. A system fordetermining a successive interference cancellation (SIC) ordering for aplurality of codewords, the system comprising processing circuitry for:mapping, for each ordering of a plurality of SIC orderings of thecodewords, an effective channel quality associated with each codeword toa Packet Error Rate (PER) value associated with the each codeword,wherein said mapping is based on a position of the each codeword in theeach ordering; and selecting, using decoder circuitry, one ordering fromthe plurality of SIC orderings for decoding the codewords using SIC,wherein said selecting is based on a performance objective computed fromthe PER value associated with the each codeword.
 12. The system of claim11, further comprising receiver circuitry for receiving the plurality ofcodewords, wherein said processing circuitry is further configured for:identifying the plurality of SIC orderings for the received codewords,wherein each ordering defines a unique order of the received codewordsfor decoding using SIC; and performing said mapping the effectivechannel quality for the each ordering after receiving the plurality ofcodewords, based on the identified plurality of SIC orderings.
 13. Thesystem of claim 11, wherein said processing circuitry is configured forcomputing one of the group consisting of a predicted Packet Error Rate(PER) value, a Signal-to-Interference and Noise Ratio (SINR) value, achannel gain, and an achievable data rate for the each codeword.
 14. Thesystem of claim 11, wherein said processing circuitry is configured for:mapping the effective channel quality associated with the each codewordto an effective stream Signal-to-Noise ratio (SNR) value for each streamof a plurality of streams associated with the each codeword; combiningeffective stream SNR values for the plurality of streams associated withthe each codeword to generate an effective codeword SNR value for theeach codeword; and mapping the effective codeword SNR value to theperformance objective associated with the each codeword.
 15. The systemof claim 11, wherein said processing circuitry is configured forcomputing a unique performance component for the each codeword at aparticular position in the each ordering.
 16. The system of claim 15,wherein said processing circuitry is configured for generating a tree ofnodes arranged in a plurality of layers associated with respective SICstages, wherein each node at a particular layer represents the uniqueperformance component for a codeword to be decoded in a SIC stageassociated with the particular layer.
 17. The system of claim 16,wherein said processing circuitry is configured for: generating a firstlayer of the tree, wherein the first layer comprises a plurality ofnodes associated with respective ones of the codewords; and generating achild node of the node N, wherein a path from a root node of the tree tothe node N comprises at least one path node, and wherein the child nodeis associated with a codeword other than at least one codewordassociated with the at least one path node.
 18. The system of claim 15,wherein said processing circuitry is configured for computing aperformance component for a codeword in the each ordering in response todetermining that the codeword is preceded by a unique partition ofcodewords in the each ordering.
 19. The system of claim 18, wherein saidprocessing circuitry is configured for: generating a first layer of thetree, wherein the first layer comprises a plurality of nodes associatedwith respective ones of the codewords; for a node N of the treeassociated with a codeword i, determining if a parent node of the node Nis associated with a codeword j where j is larger than i; and inresponse to determining that the parent node of node N is associatedwith a codeword j where j is larger than i, generating a child node ofthe node N, wherein a path from a root node of the tree to the node Ncomprises at least one path node, and wherein the child node isassociated with a codeword other than at least one codeword associatedwith the at least one path node.
 20. The system of claim 11, wherein theperformance objective is based on at least one selected from the groupconsisting of: a total throughput for the codewords in the eachordering; a total decoding probability for the codewords in the eachordering; a total Signal-to-Interference and Noise Ratio (SINR) for thecodewords in the each ordering; a total channel gain for the codewordsin the each ordering; a minimal throughput for a codeword among allcodewords in the each ordering; a lowest decoding probability for acodeword among all codewords in the each ordering; a minimal SINR for acodeword among all codewords in the each ordering; and a minimal channelgain for a codeword among all codewords in the each ordering.